Graph-theoretical analysis of EEG functional brain network development in preterm infants

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Computational Neuroscience

Background:

• Preterm birth can disrupt normal brain development.
• Understanding brain network maturation is crucial for identifying developmental trajectories and potential biomarkers.

Purpose of the Study:

• To investigate preterm brain network maturation using electroencephalography (EEG) connectivity and graph theory.
• To identify critical neurodevelopmental transitions and potential biomarkers for preterm infants.

Main Methods:

• Sixty-one preterm infants (25-36 weeks gestational age) underwent resting-state EEG recordings.
• Weighted phase-lag index (wPLI) and graph metrics were computed across different frequency bands (δ, θ, α, β).
• Developmental trajectories were modeled using generalized additive models and linear mixed-effects models.

Main Results:

• Significant interhemispheric frontopolar synchrony was observed in the weighted phase-lag index (wPLI).
• Theta-band clustering peaked at 36 weeks postmenstrual age (PMA), while beta and theta networks declined between 35-38 weeks PMA.
• Delta-band connectivity showed enhanced global integration and efficiency, with PMA-dependent increases in parietal hub betweenness, degree, and nodal efficiency in the theta band.

Conclusions:

• Thirty-six weeks PMA represents a critical neurodevelopmental transition in preterm infants.
• Emergence of delta-band hubs and parietal theta-band integration may serve as potential biomarkers for preterm brain network maturation.